The 2019-2020 Coronavirus Pandemic Analysis

Contact: Smith Research

BACKGROUND & APPROACH

I wanted to track and trend the coronavirus outbreak on my own curiosity. There are some interesting questions that may fall out of this, as it is a very historic moment, including scientifically and analytically (we have a large amount of data being shared across the globe, analyzed in real-time). The world has come to a halt because of it.
This analysis attempts to answer the following questions (more to come):

  1. What does the trend of the pandemic look like to date?
  2. What are future case predictions based on historical model?
  3. What interesting quirks or patterns emerge?

ASSUMPTIONS & LIMITATIONS: * This data is limited by the source. I realized early on that depending on source there were conflicting # of cases. Originally I was using JHU data… but this was always ‘ahead’ of the Our World In Data. I noticed that JHU’s website was buggy- you clicked on the U.S. stats but it didn’t reflect the U.S.. So I changed data sources to be more consistent with what is presented in the media (and Our World In Data has more extensive plots I can compare my own to). An interesting aside might be why the discrepancy? Was I missing something?
* Defintiions are important as is the idea that multiple varibales accumulate in things like total cases (more testing for example).

SOURCE RAW DATA: * https://ourworldindata.org/coronavirus
* https://github.com/CSSEGISandData/COVID-19/
*

INPUT DATA LOCATION: github (https://github.com/sbs87/coronavirus/tree/master/data)

OUTPUT DATA LOCATIOn: github (https://github.com/sbs87/coronavirus/tree/master/results)

TIMESTAMP

Start: ##—— Thu May 21 09:21:33 2020 ——##

PRE-ANALYSIS

The following sections are outside the scope of the ‘analysis’ but are still needed to prepare everything

UPSTREAM PROCESSING/ANALYSIS

  1. Google Mobility Scraping, script available at get_google_mobility.py
# Mobility data has to be extracted from Google PDF reports using a web scraping script (python , written by Peter Simone, https://github.com/petersim1/MIT_COVID19)

# See get_google_mobility.py for local script 

python3 get_google_mobility.py
# writes csv file of mobility data as "mobility.csv"

SET UP ENVIORNMENT

Load libraries and set global variables

# timestamp start
timestamp()
## ##------ Thu May 21 09:21:33 2020 ------##

# clear previous enviornment
rm(list = ls())

##------------------------------------------
## LIBRARIES
##------------------------------------------
library(plyr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  3.0.0     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::arrange()   masks plyr::arrange()
## x purrr::compact()   masks plyr::compact()
## x dplyr::count()     masks plyr::count()
## x dplyr::failwith()  masks plyr::failwith()
## x dplyr::filter()    masks stats::filter()
## x dplyr::id()        masks plyr::id()
## x dplyr::lag()       masks stats::lag()
## x dplyr::mutate()    masks plyr::mutate()
## x dplyr::rename()    masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()
library(ggplot2)
library(reshape2)
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
library(plot.utils)
library(utils)
library(knitr)

##------------------------------------------

##------------------------------------------
# GLOBAL VARIABLES
##------------------------------------------
user_name <- Sys.info()["user"]
working_dir <- paste0("/Users/", user_name, "/Projects/coronavirus/")  # don't forget trailing /
results_dir <- paste0(working_dir, "results/")  # assumes diretory exists
results_dir_custom <- paste0(results_dir, "custom/")  # assumes diretory exists


Corona_Cases.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
Corona_Cases.US.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv"
Corona_Deaths.US.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv"
Corona_Deaths.source_url <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"

Corona_Cases.fn <- paste0(working_dir, "data/", basename(Corona_Cases.source_url))
Corona_Cases.US.fn <- paste0(working_dir, "data/", basename(Corona_Cases.US.source_url))
Corona_Deaths.fn <- paste0(working_dir, "data/", basename(Corona_Deaths.source_url))
Corona_Deaths.US.fn <- paste0(working_dir, "data/", basename(Corona_Deaths.US.source_url))
default_theme <- theme_bw() + theme(text = element_text(size = 14))  # fix this
##------------------------------------------

FUNCTIONS

List of functions

function_name description
prediction_model outputs case estumate for given log-linear moder parameters slope and intercept
make_long converts input data to long format (specialized cases)
name_overlaps outputs the column names intersection and set diffs of two data frame
find_linear_index finds the first date at which linearaity occurs
##------------------------------------------
## FUNCTION: prediction_model
##------------------------------------------
## --- //// ----
# Takes days vs log10 (case) linear model parameters and a set of days since 100 cases and outputs a dataframe with total number of predicted cases for those days
## --- //// ----
prediction_model<-function(m=1,b=0,days=1){
  total_cases<-m*days+b
  total_cases.log<-log(total_cases,10)
  prediction<-data.frame(days=days,Total_confirmed_cases_perstate=total_cases)
  return(prediction)
}
##------------------------------------------

##------------------------------------------
## FUNCTION: make_long
##------------------------------------------
## --- //// ----
# Takes wide-format case data and converts into long format, using date and total cases as variable/values. Also enforces standardization/assumes data struture naming by using fixed variable name, value name, id.vars, 
## --- //// ----
make_long<-function(data_in,variable.name = "Date",
                   value.name = "Total_confirmed_cases",
                   id.vars=c("case_type","Province.State","Country.Region","Lat","Long","City","Population")){

long_data<-melt(data_in,
                id.vars = id.vars,
                variable.name=variable.name,
                value.name=value.name)
return(long_data)

}
##------------------------------------------

## THIS WILL BE IN UTILS AT SOME POINT
name_overlaps<-function(df1,df2){
i<-intersect(names(df1),
names(df2))
sd1<-setdiff(names(df1),
names(df2))
sd2<-setdiff(names(df2),names(df1))
cat("intersection:\n",paste(i,"\n"))
cat("in df1 but not df2:\n",paste(sd1,"\n"))
cat("in df2 but not df1:\n",paste(sd2,"\n"))
return(list("int"=i,"sd_1_2"=sd1,"sd_2_1"=sd2))
}

##------------------------------------------

##------------------------------------------
## FUNCTION: find_linear_index
##------------------------------------------
## --- //// ----
# Find date at which total case data is linear (for a given data frame) 
## --- //// ----

find_linear_index<-function(tmp,running_avg=5){
  tmp$Total_confirmed_cases_perstate.log<-log(tmp$Total_confirmed_cases_perstate,2)
  derivative<-data.frame(matrix(nrow = nrow(tmp),ncol = 4))
  names(derivative)<-c("m.time","mm.time","cumsum","date")
  
  # First derivative
  for(t in 2:nrow(tmp)){
    slope.t<- tmp[t,"Total_confirmed_cases_perstate.log"]- tmp[t-1,"Total_confirmed_cases_perstate.log"]
    derivative[t,"m.time"]<-slope.t
    derivative[t,"date"]<-as.Date(tmp[t,"Date"])
  }
  
  # Second derivative
  for(t in 2:nrow(derivative)){
    slope.t<- derivative[t,"m.time"]- derivative[t-1,"m.time"]
    derivative[t,"mm.time"]<-slope.t
  }
  
  #Compute running sum of second derivative (window = 5). Choose point at which within 0.2
  for(t in running_avg:nrow(derivative)){
    slope.t<- sum(abs(derivative[t:(t-4),"mm.time"])<0.2,na.rm = T)
    derivative[t,"cumsum"]<-slope.t
  }
  
  #Find date -5 from the stablility point
  linear_begin<-min(derivative[!is.na(derivative$cumsum) & derivative$cumsum==running_avg,"date"])-running_avg
  
  return(linear_begin)
}

READ IN DATA

# Q: do we want to archive previous versions? Maybe an auto git mv?

##------------------------------------------
## Download and read in latest data from github
##------------------------------------------
download.file(Corona_Cases.source_url, destfile = Corona_Cases.fn)
Corona_Totals.raw <- read.csv(Corona_Cases.fn, header = T, stringsAsFactors = F)

download.file(Corona_Cases.US.source_url, destfile = Corona_Cases.US.fn)
Corona_Totals.US.raw <- read.csv(Corona_Cases.US.fn, header = T, stringsAsFactors = F)

download.file(Corona_Deaths.source_url, destfile = Corona_Deaths.fn)
Corona_Deaths.raw <- read.csv(Corona_Deaths.fn, header = T, stringsAsFactors = F)

download.file(Corona_Deaths.US.source_url, destfile = Corona_Deaths.US.fn)
Corona_Deaths.US.raw <- read.csv(Corona_Deaths.US.fn, header = T, stringsAsFactors = F)

# latest date on all data:
paste("US deaths:", names(Corona_Deaths.US.raw)[ncol(Corona_Deaths.US.raw)])
## [1] "US deaths: X5.20.20"
paste("US total:", names(Corona_Totals.US.raw)[ncol(Corona_Totals.US.raw)])
## [1] "US total: X5.20.20"
paste("World deaths:", names(Corona_Deaths.raw)[ncol(Corona_Deaths.raw)])
## [1] "World deaths: X5.20.20"
paste("World total:", names(Corona_Totals.raw)[ncol(Corona_Totals.raw)])
## [1] "World total: X5.20.20"

PROCESS DATA

  • Convert to long format
  • Fix date formatting/convert to numeric date
  • Log10 transform total # cases
##------------------------------------------
## Combine death and total data frames
##------------------------------------------
Corona_Totals.raw$case_type<-"total"
Corona_Totals.US.raw$case_type<-"total"
Corona_Deaths.raw$case_type<-"death"
Corona_Deaths.US.raw$case_type<-"death"

# for some reason, Population listed in US death file but not for other data... Weird. When combining, all datasets will have this column, but US deaths is the only useful one.  
Corona_Totals.US.raw$Population<-"NA" 
Corona_Totals.raw$Population<-"NA"
Corona_Deaths.raw$Population<-"NA"

Corona_Cases.raw<-rbind(Corona_Totals.raw,Corona_Deaths.raw)
Corona_Cases.US.raw<-rbind(Corona_Totals.US.raw,Corona_Deaths.US.raw)
#TODO: custom utils- setdiff, intersect names... option to output in merging too
##------------------------------------------
# prepare raw datasets for eventual combining
##------------------------------------------
Corona_Cases.raw$City<-"NA" # US-level data has Cities
Corona_Cases.US.raw$Country_Region<-"US_state" # To differentiate from World-level stats

Corona_Cases.US.raw<-plyr::rename(Corona_Cases.US.raw,c("Province_State"="Province.State",
                                                  "Country_Region"="Country.Region",
                                                  "Long_"="Long",
                                                  "Admin2"="City"))


##------------------------------------------
## Convert to long format
##------------------------------------------
#JHU has a gross file format. It's in wide format with each column is the date in MM/DD/YY. So read this in as raw data but trasnform it to be better suited for analysis
# Furthermore, the World and US level data is formatted differently, containing different columns, etc. Recitfy this and combine the world-level stats with U.S. level stats.

Corona_Cases.long<-rbind(make_long(select(Corona_Cases.US.raw,-c(UID,iso2,iso3,code3,FIPS,Combined_Key))),
make_long(Corona_Cases.raw))


##------------------------------------------
## Fix date formatting, convert to numeric date
##------------------------------------------
Corona_Cases.long$Date<-gsub(Corona_Cases.long$Date,pattern = "^X",replacement = "0") # leading 0 read in as X
Corona_Cases.long$Date<-gsub(Corona_Cases.long$Date,pattern = "20$",replacement = "2020") # ends in .20 and not 2020
Corona_Cases.long$Date<-as.Date(Corona_Cases.long$Date,format = "%m.%d.%y")
Corona_Cases.long$Date.numeric<-as.numeric(Corona_Cases.long$Date)

kable(table(select(Corona_Cases.long,c("Country.Region","case_type"))),caption = "Number of death and total case longitudinal datapoints per geographical region")
Number of death and total case longitudinal datapoints per geographical region
death total
Afghanistan 120 120
Albania 120 120
Algeria 120 120
Andorra 120 120
Angola 120 120
Antigua and Barbuda 120 120
Argentina 120 120
Armenia 120 120
Australia 960 960
Austria 120 120
Azerbaijan 120 120
Bahamas 120 120
Bahrain 120 120
Bangladesh 120 120
Barbados 120 120
Belarus 120 120
Belgium 120 120
Belize 120 120
Benin 120 120
Bhutan 120 120
Bolivia 120 120
Bosnia and Herzegovina 120 120
Botswana 120 120
Brazil 120 120
Brunei 120 120
Bulgaria 120 120
Burkina Faso 120 120
Burma 120 120
Burundi 120 120
Cabo Verde 120 120
Cambodia 120 120
Cameroon 120 120
Canada 1680 1680
Central African Republic 120 120
Chad 120 120
Chile 120 120
China 3960 3960
Colombia 120 120
Comoros 120 120
Congo (Brazzaville) 120 120
Congo (Kinshasa) 120 120
Costa Rica 120 120
Cote d’Ivoire 120 120
Croatia 120 120
Cuba 120 120
Cyprus 120 120
Czechia 120 120
Denmark 360 360
Diamond Princess 120 120
Djibouti 120 120
Dominica 120 120
Dominican Republic 120 120
Ecuador 120 120
Egypt 120 120
El Salvador 120 120
Equatorial Guinea 120 120
Eritrea 120 120
Estonia 120 120
Eswatini 120 120
Ethiopia 120 120
Fiji 120 120
Finland 120 120
France 1320 1320
Gabon 120 120
Gambia 120 120
Georgia 120 120
Germany 120 120
Ghana 120 120
Greece 120 120
Grenada 120 120
Guatemala 120 120
Guinea 120 120
Guinea-Bissau 120 120
Guyana 120 120
Haiti 120 120
Holy See 120 120
Honduras 120 120
Hungary 120 120
Iceland 120 120
India 120 120
Indonesia 120 120
Iran 120 120
Iraq 120 120
Ireland 120 120
Israel 120 120
Italy 120 120
Jamaica 120 120
Japan 120 120
Jordan 120 120
Kazakhstan 120 120
Kenya 120 120
Korea, South 120 120
Kosovo 120 120
Kuwait 120 120
Kyrgyzstan 120 120
Laos 120 120
Latvia 120 120
Lebanon 120 120
Lesotho 120 120
Liberia 120 120
Libya 120 120
Liechtenstein 120 120
Lithuania 120 120
Luxembourg 120 120
Madagascar 120 120
Malawi 120 120
Malaysia 120 120
Maldives 120 120
Mali 120 120
Malta 120 120
Mauritania 120 120
Mauritius 120 120
Mexico 120 120
Moldova 120 120
Monaco 120 120
Mongolia 120 120
Montenegro 120 120
Morocco 120 120
Mozambique 120 120
MS Zaandam 120 120
Namibia 120 120
Nepal 120 120
Netherlands 600 600
New Zealand 120 120
Nicaragua 120 120
Niger 120 120
Nigeria 120 120
North Macedonia 120 120
Norway 120 120
Oman 120 120
Pakistan 120 120
Panama 120 120
Papua New Guinea 120 120
Paraguay 120 120
Peru 120 120
Philippines 120 120
Poland 120 120
Portugal 120 120
Qatar 120 120
Romania 120 120
Russia 120 120
Rwanda 120 120
Saint Kitts and Nevis 120 120
Saint Lucia 120 120
Saint Vincent and the Grenadines 120 120
San Marino 120 120
Sao Tome and Principe 120 120
Saudi Arabia 120 120
Senegal 120 120
Serbia 120 120
Seychelles 120 120
Sierra Leone 120 120
Singapore 120 120
Slovakia 120 120
Slovenia 120 120
Somalia 120 120
South Africa 120 120
South Sudan 120 120
Spain 120 120
Sri Lanka 120 120
Sudan 120 120
Suriname 120 120
Sweden 120 120
Switzerland 120 120
Syria 120 120
Taiwan* 120 120
Tajikistan 120 120
Tanzania 120 120
Thailand 120 120
Timor-Leste 120 120
Togo 120 120
Trinidad and Tobago 120 120
Tunisia 120 120
Turkey 120 120
Uganda 120 120
Ukraine 120 120
United Arab Emirates 120 120
United Kingdom 1320 1320
Uruguay 120 120
US 120 120
US_state 391320 391320
Uzbekistan 120 120
Venezuela 120 120
Vietnam 120 120
West Bank and Gaza 120 120
Western Sahara 120 120
Yemen 120 120
Zambia 120 120
Zimbabwe 120 120
# Decouple population and lat/long data, refactor to make it more tidy
metadata_columns<-c("Lat","Long","Population")
metadata<-unique(select(filter(Corona_Cases.long,case_type=="death"),c("Country.Region","Province.State","City",all_of(metadata_columns))))
Corona_Cases.long<-select(Corona_Cases.long,-all_of(metadata_columns))

# Some counties are not summarized on the country level. collapse all but US
Corona_Cases.long<-rbind.fill(ddply(filter(Corona_Cases.long,!Country.Region=="US_state"),c("case_type","Country.Region","Date","Date.numeric"),summarise,Total_confirmed_cases=sum(Total_confirmed_cases)),filter(Corona_Cases.long,Country.Region=="US_state"))

# Put total case and deaths side-by-side (wide)
Corona_Cases<-spread(Corona_Cases.long,key = case_type,value = Total_confirmed_cases)

#Compute moratlity rate
Corona_Cases$mortality_rate<-Corona_Cases$death/Corona_Cases$total

#TMP
Corona_Cases<-plyr::rename(Corona_Cases,c("total"="Total_confirmed_cases","death"="Total_confirmed_deaths"))

##------------------------------------------
## log10 transform total # cases
##------------------------------------------
Corona_Cases$Total_confirmed_cases.log<-log(Corona_Cases$Total_confirmed_cases,10)
Corona_Cases$Total_confirmed_deaths.log<-log(Corona_Cases$Total_confirmed_deaths,10)
##------------------------------------------
       
##------------------------------------------
## Compute # of days since 100th for US data
##------------------------------------------

# Find day that 100th case was found for Country/Province. NOTE: Non US countries may have weird provinces. For example, Frane is summairzed at the country level but also had 3 providences. I've only ensured the U.S. case100 works... so the case100_date for U.S. is summarized both for the entire country (regardless of state) and on a per-state level. 
# TODO: consider city-level summary as well. This data may be sparse

Corona_Cases<-merge(Corona_Cases,ddply(filter(Corona_Cases,Total_confirmed_cases>100),c("Country.Region"),summarise,case100_date=min(Date.numeric)))
Corona_Cases$Days_since_100<-Corona_Cases$Date.numeric-Corona_Cases$case100_date

##------------------------------------------
## Add population and lat/long data (CURRENTLY US ONLY)
##------------------------------------------

kable(filter(metadata,(is.na(Country.Region) | is.na(Population) )) %>% select(c("Country.Region","Province.State","City")) %>% unique(),caption = "Regions for which either population or Country is NA")
Regions for which either population or Country is NA
Country.Region Province.State City
# Drop missing data 
metadata<-filter(metadata,!(is.na(Country.Region) | is.na(Population) ))
# Convert remaining pop to numeric
metadata$Population<-as.numeric(metadata$Population)
## Warning: NAs introduced by coercion
# Add metadata to cases
Corona_Cases<-merge(Corona_Cases,metadata,all.x = T)

##------------------------------------------
## Compute total and death cases relative to population 
##------------------------------------------

Corona_Cases$Total_confirmed_cases.per100<-100*Corona_Cases$Total_confirmed_cases/Corona_Cases$Population
Corona_Cases$Total_confirmed_deaths.per100<-100*Corona_Cases$Total_confirmed_deaths/Corona_Cases$Population


##------------------------------------------
## Filter df for US state-wide stats
##------------------------------------------

Corona_Cases.US_state<-filter(Corona_Cases,Country.Region=="US_state" & Total_confirmed_cases>0 ) 
kable(table(select(Corona_Cases.US_state,c("Province.State"))),caption = "Number of longitudinal datapoints (total/death) per state")
Number of longitudinal datapoints (total/death) per state
Var1 Freq
Alabama 3864
Alaska 702
Arizona 986
Arkansas 4005
California 3765
Colorado 3523
Connecticut 583
Delaware 239
Diamond Princess 65
District of Columbia 66
Florida 4158
Georgia 9205
Grand Princess 66
Guam 66
Hawaii 342
Idaho 1826
Illinois 5196
Indiana 5365
Iowa 4787
Kansas 3910
Kentucky 5765
Louisiana 3890
Maine 985
Maryland 1529
Massachusetts 1001
Michigan 4666
Minnesota 4335
Mississippi 4819
Missouri 5235
Montana 1640
Nebraska 2904
Nevada 723
New Hampshire 686
New Jersey 1490
New Mexico 1588
New York 3743
North Carolina 5636
North Dakota 1841
Northern Mariana Islands 51
Ohio 5037
Oklahoma 3757
Oregon 1943
Pennsylvania 3998
Puerto Rico 66
Rhode Island 388
South Carolina 2823
South Dakota 2362
Tennessee 5428
Texas 11187
Utah 915
Vermont 915
Virgin Islands 66
Virginia 7072
Washington 2566
West Virginia 2533
Wisconsin 3770
Wyoming 1165
Corona_Cases.US_state<-merge(Corona_Cases.US_state,ddply(filter(Corona_Cases.US_state,Total_confirmed_cases>100),c("Province.State"),summarise,case100_date_state=min(Date.numeric)))
Corona_Cases.US_state$Days_since_100_state<-Corona_Cases.US_state$Date.numeric-Corona_Cases.US_state$case100_date_state

ANALYSIS

Q1: What is the trend in cases, mortality across geopgraphical regions?

Plot # of cases vs time
* For each geographical set:
* comparative longitudinal case trend (absolute & log scale)
* comparative longitudinal mortality trend
* death vs total correlation

question dataset x y color facet pch dimentions
comparative_longitudinal_case_trend long time log_cases geography none (case type?) case_type [15, 50, 4] geography x (2 scale?) case type
comparative longitudinal case trend long time cases geography case_type ? [15, 50, 4] geography x (2+ scale) case type
comparative longitudinal mortality trend wide time mortality rate geography none none [15, 50, 4] geography
death vs total correlation wide cases deaths geography none none [15, 50, 4] geography
# total cases vs time
# death cases vs time
# mortality rate vs time
# death vs mortality


  # death vs mortality
  # total & death case vs time (same plot)

#<question> <x> <y> <colored> <facet> <dataset>
## trend in case/deaths over time, comapred across regions <time> <log cases> <geography*> <none> <.wide>
## trend in case/deaths over time, comapred across regions <time> <cases> <geography*> <case_type> <.long>
## trend in mortality rate over time, comapred across regions <time> <mortality rate> <geography*> <none>
## how are death/mortality related/correlated? <time> <log cases> <geography*> <none>
## how are death and case load correlated? <cases> <deaths>

# lm for each?? - > apply lm from each region starting from 100th case. m, b associated with each.
    # input: geographical regsion, logcase vs day (100th case)
    # output: m, b for each geographical region ID



#total/death on same plot-  diffeer by 2 logs, so when plotting log, use pch. when plotting absolute, need to use free scales
#when plotting death and case on same, melt. 

#CoronaCases - > filter sets (3)
  #world - choose countries with sufficent data

N<-ddply(filter(Corona_Cases,Total_confirmed_cases>100),c("Country.Region"),summarise,n=length(Country.Region))
ggplot(filter(N,n<100),aes(x=n))+
  geom_histogram()+
  default_theme+
  ggtitle("Distribution of number of days with at least 100 confirmed cases for each region")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

kable(arrange(N,-n),caption="Sorted number of days with at least 100 confirmed cases")
Sorted number of days with at least 100 confirmed cases
Country.Region n
US_state 32639
China 120
Diamond Princess 101
Korea, South 91
Japan 90
Italy 88
Iran 85
Singapore 82
France 81
Germany 81
Spain 80
US 79
Switzerland 77
United Kingdom 77
Belgium 76
Netherlands 76
Norway 76
Sweden 76
Austria 74
Malaysia 73
Australia 72
Bahrain 72
Denmark 72
Canada 71
Qatar 71
Iceland 70
Brazil 69
Czechia 69
Finland 69
Greece 69
Iraq 69
Israel 69
Portugal 69
Slovenia 69
Egypt 68
Estonia 68
India 68
Ireland 68
Kuwait 68
Philippines 68
Poland 68
Romania 68
Saudi Arabia 68
Indonesia 67
Lebanon 67
San Marino 67
Thailand 67
Chile 66
Pakistan 66
Luxembourg 65
Peru 65
Russia 65
Ecuador 64
Mexico 64
Slovakia 64
South Africa 64
United Arab Emirates 64
Armenia 63
Colombia 63
Croatia 63
Panama 63
Serbia 63
Taiwan* 63
Turkey 63
Argentina 62
Bulgaria 62
Latvia 62
Uruguay 62
Algeria 61
Costa Rica 61
Dominican Republic 61
Hungary 61
Andorra 60
Bosnia and Herzegovina 60
Jordan 60
Lithuania 60
Morocco 60
New Zealand 60
North Macedonia 60
Vietnam 60
Albania 59
Cyprus 59
Malta 59
Moldova 59
Brunei 58
Burkina Faso 58
Sri Lanka 58
Tunisia 58
Ukraine 57
Azerbaijan 56
Ghana 56
Kazakhstan 56
Oman 56
Senegal 56
Venezuela 56
Afghanistan 55
Cote d’Ivoire 55
Cuba 54
Mauritius 54
Uzbekistan 54
Cambodia 53
Cameroon 53
Honduras 53
Nigeria 53
West Bank and Gaza 53
Belarus 52
Georgia 52
Bolivia 51
Kosovo 51
Kyrgyzstan 51
Montenegro 51
Congo (Kinshasa) 50
Kenya 49
Niger 48
Guinea 47
Rwanda 47
Trinidad and Tobago 47
Paraguay 46
Bangladesh 45
Djibouti 43
El Salvador 42
Guatemala 41
Madagascar 40
Mali 39
Congo (Brazzaville) 36
Jamaica 36
Gabon 34
Somalia 34
Tanzania 34
Ethiopia 33
Burma 32
Sudan 31
Liberia 30
Maldives 28
Equatorial Guinea 27
Cabo Verde 25
Sierra Leone 23
Guinea-Bissau 22
Togo 22
Zambia 21
Eswatini 20
Chad 19
Tajikistan 18
Haiti 16
Sao Tome and Principe 16
Benin 14
Nepal 14
Uganda 14
Central African Republic 13
South Sudan 13
Guyana 11
Mozambique 10
Yemen 6
Mongolia 5
Mauritania 2
Nicaragua 2
# Pick top 15 countries with data
max_colors<-12
# find way to fix this- China has diff provences. Plot doesnt look right...
sufficient_data<-arrange(filter(N,!Country.Region %in% c("US_state", "Diamond Princess")),-n)[1:max_colors,]
kable(sufficient_data,caption = paste0("Top ",max_colors," countries with sufficient data"))
Top 12 countries with sufficient data
Country.Region n
China 120
Korea, South 91
Japan 90
Italy 88
Iran 85
Singapore 82
France 81
Germany 81
Spain 80
US 79
Switzerland 77
United Kingdom 77
Corona_Cases.world<-filter(Corona_Cases,Country.Region %in% c(sufficient_data$Country.Region))


  #us 
  #    - by state
Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
# summarize 
#!City %in% c("Unassigned") 
  #    - specific cities
#mortality_rate!=Inf & mortality_rate<=1
Corona_Cases.UScity<-filter(Corona_Cases,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") & City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May"))

measure_vars_long<-c("Total_confirmed_cases.log","Total_confirmed_cases","Total_confirmed_deaths","Total_confirmed_deaths.log")
melt_arg_list<-list(variable.name = "case_type",value.name = "cases",measure.vars = c("Total_confirmed_cases","Total_confirmed_deaths"))
melt_arg_list$data=NULL


melt_arg_list$data=select(Corona_Cases.world,-ends_with(match = "log"))
Corona_Cases.world.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.UScity,-ends_with(match = "log"))
Corona_Cases.UScity.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.US_state,-ends_with(match = "log"))
Corona_Cases.US_state.long<-do.call(melt,melt_arg_list)

Corona_Cases.world.long$cases.log<-log(Corona_Cases.world.long$cases,10)
Corona_Cases.US_state.long$cases.log<-log(Corona_Cases.US_state.long$cases,10)
Corona_Cases.UScity.long$cases.log<-log(Corona_Cases.UScity.long$cases,10)


# what is the current death and total case load for US? For world? For states?
#-absolute
#-log

# what is mortality rate (US, world)
#-absolute

#how is death and case correlated? (US, world)
#-absolute
#Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
#Corona_Cases.US.case100<-filter(Corona_Cases.US, Days_since_100>=0)
# linear model parameters
#(model_fit<-lm(formula = Total_confirmed_cases.log~Days_since_100,data= Corona_Cases.US.case100 ))

#(slope<-model_fit$coefficients[2])
#(intercept<-model_fit$coefficients[1])

# Correlation coefficient
#cor(x = Corona_Cases.US.case100$Days_since_100,y = Corona_Cases.US.case100$Total_confirmed_cases.log)

##------------------------------------------
## Plot World Data
##------------------------------------------
# Timestamp for world
timestamp_plot.world<-paste("Most recent date for which data available:",max(Corona_Cases.world$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")


# Base template for plots
baseplot.world<-ggplot(data=NULL,aes(x=Days_since_100,col=Country.Region))+
  default_theme+
  scale_color_brewer(type = "qualitative",palette = "Paired")+
  ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
  theme(legend.position = "bottom",plot.title = element_text(size=12))


##/////////////////////////
### Plot Longitudinal cases

(Corona_Cases.world.long.plot<-baseplot.world+
    geom_point(data=Corona_Cases.world.long,aes(y=cases))+
    geom_line(data=Corona_Cases.world.long,aes(y=cases))+
    facet_wrap(~case_type,scales = "free_y",ncol=1)+
    ggtitle(timestamp_plot.world)
    )

(Corona_Cases.world.loglong.plot<-baseplot.world+
    geom_point(data=Corona_Cases.world.long,aes(y=cases.log))+
    geom_line(data=Corona_Cases.world.long,aes(y=cases.log))+
    facet_wrap(~case_type,scales = "free_y",ncol=1)+
    ggtitle(timestamp_plot.world))

##/////////////////////////
### Plot Longitudinal mortality rate

(Corona_Cases.world.mortality.plot<-baseplot.world+
    geom_point(data=Corona_Cases.world,aes(y=mortality_rate))+
    geom_line(data=Corona_Cases.world,aes(y=mortality_rate))+
    ylim(c(0,0.3))+
    ggtitle(timestamp_plot.world))
## Warning: Removed 100 rows containing missing values (geom_point).
## Warning: Removed 100 row(s) containing missing values (geom_path).

##/////////////////////////
### Plot death vs total case correlation

(Corona_Cases.world.casecor.plot<-ggplot(Corona_Cases.world,aes(x=Total_confirmed_cases,y=Total_confirmed_deaths,col=Country.Region))+
  geom_point()+
  geom_line()+
  default_theme+
  scale_color_brewer(type = "qualitative",palette = "Paired")+
  ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
  theme(legend.position = "bottom",plot.title = element_text(size=12))+
    ggtitle(timestamp_plot.world))

### Write polots

write_plot(Corona_Cases.world.long.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.long.plot.png"
write_plot(Corona_Cases.world.loglong.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.loglong.plot.png"
write_plot(Corona_Cases.world.mortality.plot,wd = results_dir)
## Warning: Removed 100 rows containing missing values (geom_point).

## Warning: Removed 100 row(s) containing missing values (geom_path).
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.mortality.plot.png"
write_plot(Corona_Cases.world.casecor.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.casecor.plot.png"
##------------------------------------------
## Plot US State Data
##-----------------------------------------

baseplot.US<-ggplot(data=NULL,aes(x=Days_since_100_state,col=case_type))+
  default_theme+
  facet_wrap(~Province.State)+
  ggtitle(paste("Log10 cases over time,",timestamp_plot.world))

Corona_Cases.US_state.long.plot<-baseplot.US+geom_point(data=Corona_Cases.US_state.long,aes(y=cases.log))
##------------------------------------------
## Plot US City Data
##-----------------------------------------

Corona_Cases.US.plotdata<-filter(Corona_Cases.US_state,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") &
                                   City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May") &
                                   Total_confirmed_cases>0) 
timestamp_plot<-paste("Most recent date for which data available:",max(Corona_Cases.US.plotdata$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")

city_colors<-c("Bucks"='#beaed4',"Baltimore City"='#386cb0', "New York"='#7fc97f',"Burlington"='#fdc086',"Cape May"="#e78ac3")

##/////////////////////////
### Plot death vs total case correlation

(Corona_Cases.city.loglong.plot<-ggplot(melt(Corona_Cases.US.plotdata,measure.vars = c("Total_confirmed_cases.log","Total_confirmed_deaths.log"),variable.name = "case_type",value.name = "cases"),aes(x=Date,y=cases,col=City,pch=case_type))+
  geom_point(size=4)+
    geom_line()+
  default_theme+
  #facet_wrap(~case_type)+
    ggtitle(paste("Log10 total and death cases over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
    scale_color_manual(values = city_colors)+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.long.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases,col=City))+
  geom_point(size=4)+
  geom_line()+
  default_theme+
  facet_grid(~Province.State,scales = "free_y")+
  ggtitle(paste("MD, PA, NJ total cases over time,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))
+
  scale_color_manual(values = city_colors)+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.mortality.plot<-ggplot(Corona_Cases.US.plotdata,aes(x=Date,y=mortality_rate,col=City))+
  geom_point(size=3)+
  geom_line(size=2)+
  default_theme+
  ggtitle(paste("Mortality rate (deaths/total) over time,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
  scale_color_manual(values = city_colors)+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.casecor.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(y=Total_confirmed_deaths,x=Total_confirmed_cases,col=City))+
  geom_point(size=3)+
  geom_line(size=2)+
  default_theme+
  ggtitle(paste("Correlation of death vs total cases,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12))+
  scale_color_manual(values = city_colors))

(Corona_Cases.city.long.normalized.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases.per100,col=City))+
  geom_point(size=4)+
  geom_line()+
  default_theme+
  facet_grid(~Province.State)+
  ggtitle(paste("MD, PA, NJ total cases over time per 100 people,",timestamp_plot))+
  theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
  scale_color_manual(values = city_colors)  +
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

write_plot(Corona_Cases.city.long.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.plot.png"
write_plot(Corona_Cases.city.loglong.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.loglong.plot.png"
write_plot(Corona_Cases.city.mortality.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.mortality.plot.png"
write_plot(Corona_Cases.city.casecor.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.casecor.plot.png"
write_plot(Corona_Cases.city.long.normalized.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.normalized.plot.png"

Q1b what is the model

Fit the cases to a linear model 1. Find time at which the case vs date becomes linear in each plot
2. Fit linear model for each city

# What is the predict # of cases for the next few days?
# How is the model performing historically?

Corona_Cases.US_state.summary<-ddply(Corona_Cases.US_state,
                                     c("Province.State","Date"),
                                     summarise,
                                     Total_confirmed_cases_perstate=sum(Total_confirmed_cases)) %>% 
    filter(Total_confirmed_cases_perstate>100)

# Compute the states with the most cases (for coloring and for linear model)
top_states_totals<-head(ddply(Corona_Cases.US_state.summary,c("Province.State"),summarise, Total_confirmed_cases_perstate.max=max(Total_confirmed_cases_perstate)) %>% arrange(-Total_confirmed_cases_perstate.max),n=max_colors)

kable(top_states_totals,caption = "Top 12 States, total count ")
top_states<-top_states_totals$Province.State

# Manually fix states so that Maryland is switched out for New York
top_states_modified<-c(top_states[top_states !="New York"],"Maryland")

# Plot with all states:
(Corona_Cases.US_state.summary.plot<-ggplot(Corona_Cases.US_state.summary,aes(x=Date,y=Total_confirmed_cases_perstate))+
  geom_point()+
  geom_point(data=filter(Corona_Cases.US_state.summary,Province.State %in% top_states),aes(col=Province.State))+
  scale_color_brewer(type = "qualitative",palette = "Paired")+
  default_theme+
  theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
  ggtitle("Total confirmed cases per state, top 12 colored")+
  scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

##------------------------------------------
## Fit linear model to time vs total cases
##-----------------------------------------

# First, find the date at which each state's cases vs time becomes lienar (2nd derivative is about 0)
li<-ddply(Corona_Cases.US_state.summary,c("Province.State"),find_linear_index)

# Compute linear model for each state starting at the point at which data becomes linear
for(i in 1:nrow(li)){
  Province.State.i<-li[i,"Province.State"]
  date.i<-li[i,"V1"]
  data.i<-filter(Corona_Cases.US_state.summary,Province.State==Province.State.i & as.numeric(Date) >= date.i)
  model_results<-lm(data.i,formula = Total_confirmed_cases_perstate~Date)
  slope<-model_results$coefficients[2]
  intercept<-model_results$coefficients[1]
  li[li$Province.State==Province.State.i,"m"]<-slope
  li[li$Province.State==Province.State.i,"b"]<-intercept
  }

# Compute top state case load with fitted model

(Corona_Cases.US_state.lm.plot<-ggplot(filter(Corona_Cases.US_state.summary,Province.State %in% top_states_modified ))+
    geom_abline(data=filter(li,Province.State %in% top_states_modified),
                aes(slope = m,intercept = b,col=Province.State),lty=2)+
    geom_point(aes(x=Date,y=Total_confirmed_cases_perstate,col=Province.State))+
    scale_color_brewer(type = "qualitative",palette = "Paired")+
    default_theme+
    theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
    ggtitle("Total confirmed cases per state, top 12 colored")+
    scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

##------------------------------------------
## Predict the number of total cases over the next week
##-----------------------------------------

predicted_days<-c(0,1,2,3,7)+as.numeric(as.Date("2020-04-20"))

predicted_days_df<-data.frame(matrix(ncol=3))
names(predicted_days_df)<-c("Province.State","days","Total_confirmed_cases_perstate")

# USe model parameters to estiamte case loads
for(state.i in top_states_modified){
  predicted_days_df<-rbind(predicted_days_df,
                           data.frame(Province.State=state.i,
                                      prediction_model(m = li[li$Province.State==state.i,"m"],
                                                       b =li[li$Province.State==state.i,"b"] ,
                                                       days =predicted_days )))
  }

predicted_days_df$Date<-as.Date(predicted_days_df$days,origin="1970-01-01")

kable(predicted_days_df,caption = "Predicted total cases over the next week for selected states")

##------------------------------------------
## Write plots
##-----------------------------------------

write_plot(Corona_Cases.US_state.summary.plot,wd = results_dir)
write_plot(Corona_Cases.US_state.lm.plot,wd = results_dir)

##------------------------------------------
## Write tables
##-----------------------------------------

write.csv(predicted_days_df,file = paste0(results_dir,"predicted_total_cases_days.csv"),quote = F,row.names = F)

Q2: What is the predicted number of cases?

What is the prediction of COVID-19 based on model thus far? Additional questions:

WHy did it take to day 40 to start a log linear trend? How long will it be till x number of cases? When will the plateu happen? Are any effects noticed with social distancing? Delays

##------------------------------------------
## Prediction and Prediction Accuracy
##------------------------------------------


today_num<-max(Corona_Cases.US$Days_since_100)
predicted_days<-today_num+c(1,2,3,7)

#mods = dlply(mydf, .(x3), lm, formula = y ~ x1 + x2)
#today:
Corona_Cases.US[Corona_Cases.US$Days_since_100==(today_num-1),]
Corona_Cases.US[Corona_Cases.US$Days_since_100==today_num,]
Corona_Cases.US$type<-"Historical"


#prediction_values<-prediction_model(m=slope,b=intercept,days = predicted_days)$Total_confirmed_cases

histoical_model<-data.frame(date=today_num,m=slope,b=intercept)
tmp<-data.frame(state=rep(c("A","B"),each=3),x=c(1,2,3,4,5,6))
tmp$y<-c(tmp[1:3,"x"]+5,tmp[4:6,"x"]*5+1)
ddply(tmp,c("state"))
lm(data =tmp,formula = y~x )

train_lm<-function(input_data,subset_coulmn,formula_input){
case_models <- dlply(input_data, subset_coulmn, lm, formula = formula_input)
case_models.parameters <- ldply(case_models, coef)
case_models.parameters<-rename(case_models.parameters,c("b"="(Intercept)","m"=subset_coulmn))
return(case_models.parameters)
}

train_lm(tmp,"state")

 dlply(input_data, subset_coulmn, lm,m=)
 
# model for previous y days
#historical_model_predictions<-data.frame(day_x=NULL,Days_since_100=NULL,Total_confirmed_cases=NULL,Total_confirmed_cases.log=NULL)
# for(i in c(1,2,3,4,5,6,7,8,9,10)){
#   #i<-1
# day_x<-today_num-i # 1, 2, 3, 4
# day_x_nextweek<-day_x+c(1,2,3)
# model_fit_x<-lm(data = filter(Corona_Cases.US.case100,Days_since_100 < day_x),formula = Total_confirmed_cases.log~Days_since_100)
# prediction_day_x_nextweek<-prediction_model(m = model_fit_x$coefficients[2],b = model_fit_x$coefficients[1],days = day_x_nextweek)
# prediction_day_x_nextweek$type<-"Predicted"
# acutal_day_x_nextweek<-filter(Corona_Cases.US,Days_since_100 %in% day_x_nextweek) %>% select(c(Days_since_100,Total_confirmed_cases,Total_confirmed_cases.log))
# acutal_day_x_nextweek$type<-"Historical"
# historical_model_predictions.i<-data.frame(day_x=day_x,rbind(acutal_day_x_nextweek,prediction_day_x_nextweek))
# historical_model_predictions<-rbind(historical_model_predictions.i,historical_model_predictions)
# }

#historical_model_predictions.withHx<-rbind.fill(historical_model_predictions,data.frame(Corona_Cases.US,type="Historical"))
#historical_model_predictions.withHx$Total_confirmed_cases.log2<-log(historical_model_predictions.withHx$Total_confirmed_cases,2)

(historical_model_predictions.plot<-ggplot(historical_model_predictions.withHx,aes(x=Days_since_100,y=Total_confirmed_cases.log,col=type))+
    geom_point(size=3)+
    default_theme+
    theme(legend.position = "bottom")+ 
      #geom_abline(slope = slope,intercept =intercept,lty=2)+
    #facet_wrap(~case_type,ncol=1)+
    scale_color_manual(values = c("Historical"="#377eb8","Predicted"="#e41a1c")))
write_plot(historical_model_predictions.plot,wd=results_dir)

Q3: What is the effect on social distancing, descreased mobility on case load?

Load data from Google which compoutes % change in user mobility relative to baseline for * Recreation
* Workplace
* Residence
* Park
* Grocery

Data from https://www.google.com/covid19/mobility/

# See pre-processing section for script on gathering mobility data

# UNDER DEVELOPMENT

mobility<-read.csv("/Users/stevensmith/Projects/MIT_COVID19/mobility.csv",header = T,stringsAsFactors = F)
#mobility$Retail_Recreation<-as.numeric(sub(mobility$Retail_Recreation,pattern = "%",replacement = ""))
#mobility$Workplace<-as.numeric(sub(mobility$Workplace,pattern = "%",replacement = ""))
#mobility$Residential<-as.numeric(sub(mobility$Residential,pattern = "%",replacement = ""))

##------------------------------------------
## Show relationship between mobility and caseload
##------------------------------------------
mobility$County<-gsub(mobility$County,pattern = " County",replacement = "")
Corona_Cases.US_state.mobility<-merge(Corona_Cases.US_state,plyr::rename(mobility,c("State"="Province.State","County"="City")))

#Corona_Cases.US_state.tmp<-merge(metadata,Corona_Cases.US_state.tmp)
# Needs to happen upsteam, see todos
#Corona_Cases.US_state.tmp$Total_confirmed_cases.perperson<-Corona_Cases.US_state.tmp$Total_confirmed_cases/as.numeric(Corona_Cases.US_state.tmp$Population)
mobility_measures<-c("Retail_Recreation","Grocery_Pharmacy","Parks","Transit","Workplace","Residential")

plot_data<-filter(Corona_Cases.US_state.mobility, Date.numeric==max(Corona_Cases.US_state$Date.numeric) ) %>% melt(measure.vars=mobility_measures) 
plot_data$value<-as.numeric(gsub(plot_data$value,pattern = "%",replacement = ""))
plot_data<-filter(plot_data,!is.na(value))

(mobility.plot<-ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
  facet_grid(Province.State~variable,scales = "free")+
  xlab("Mobility change from baseline (%)")+
  ylab(paste0("Confirmed cases per 100 people(Today)"))+
  default_theme+
  ggtitle("Mobility change vs cases"))

(mobility.global.plot<-ggplot(plot_data,aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
  facet_wrap(~variable,scales = "free")+
  xlab("Mobility change from baseline (%)")+
  ylab(paste0("Confirmed cases (Today) per 100 people"))+
  default_theme+
  ggtitle("Mobility change vs cases"))

plot_data.permobility_summary<-ddply(plot_data,c("Province.State","variable"),summarise,cor=cor(y =Total_confirmed_cases.per100,x=value),median_change=median(x=value)) %>% arrange(-abs(cor))

kable(plot_data.permobility_summary,caption = "Ranked per-state mobility correlation with total confirmed cases")
Ranked per-state mobility correlation with total confirmed cases
Province.State variable cor median_change
Alaska Transit -1.0000000 -63.0
Delaware Retail_Recreation 1.0000000 -39.5
Delaware Grocery_Pharmacy 1.0000000 -17.5
Delaware Parks -1.0000000 20.5
Delaware Transit 1.0000000 -37.0
Delaware Workplace 1.0000000 -37.0
Delaware Residential -1.0000000 14.0
Hawaii Retail_Recreation 0.9999264 -56.0
Hawaii Grocery_Pharmacy 0.9898125 -34.0
New Hampshire Parks 0.9569962 -20.0
Connecticut Grocery_Pharmacy -0.9044330 -6.0
Maine Transit -0.8967199 -50.0
Alaska Residential 0.8865220 13.0
South Dakota Parks 0.8830750 -26.0
Vermont Parks 0.8637698 -35.5
Wyoming Transit -0.8534931 -16.0
Utah Residential -0.8322895 12.0
Alaska Grocery_Pharmacy -0.8018082 -7.0
Massachusetts Workplace -0.7776214 -39.0
Utah Transit -0.7701461 -18.0
Hawaii Parks 0.7540278 -72.0
Rhode Island Workplace -0.7447468 -39.5
Connecticut Transit -0.7444543 -50.0
Alaska Workplace -0.7307515 -34.0
Hawaii Residential -0.7166003 19.0
Utah Parks -0.7153045 17.0
Hawaii Transit 0.6974840 -89.0
Utah Workplace -0.6765715 -37.0
Maine Workplace -0.6585241 -30.0
Vermont Grocery_Pharmacy -0.6507078 -25.0
Wyoming Parks -0.6461518 -4.0
New York Workplace -0.6426268 -34.5
New Jersey Workplace -0.6263512 -44.0
Rhode Island Retail_Recreation -0.6242619 -45.0
Montana Workplace -0.6239388 -40.5
Rhode Island Residential -0.6228084 18.5
Arizona Grocery_Pharmacy -0.6071971 -15.0
Nebraska Workplace 0.6065287 -32.5
North Dakota Retail_Recreation -0.5896721 -42.0
New Jersey Parks -0.5895355 -6.0
New York Retail_Recreation -0.5854996 -46.0
Massachusetts Retail_Recreation -0.5392290 -44.0
Connecticut Residential 0.5341899 14.0
New York Parks 0.5248793 20.0
New Jersey Retail_Recreation -0.5240086 -62.5
West Virginia Parks 0.5196949 -33.0
Connecticut Retail_Recreation -0.5053677 -45.0
Arkansas Parks -0.5020262 -12.0
Maine Parks 0.5019106 -31.0
Arizona Retail_Recreation -0.4965137 -42.5
Iowa Parks -0.4943176 28.5
Connecticut Workplace -0.4940100 -39.0
Montana Parks -0.4913929 -58.0
Nebraska Residential -0.4893946 14.0
Wyoming Workplace -0.4876673 -31.0
New Jersey Grocery_Pharmacy -0.4833283 2.5
Rhode Island Parks 0.4807951 52.0
New Mexico Grocery_Pharmacy -0.4784172 -11.0
Montana Residential 0.4701424 14.0
New Mexico Parks 0.4628477 -31.5
Idaho Workplace -0.4604719 -29.0
Wisconsin Transit -0.4565448 -23.5
Illinois Transit -0.4544470 -31.0
Hawaii Workplace 0.4488184 -46.0
New Mexico Residential 0.4485903 13.5
Massachusetts Grocery_Pharmacy -0.4399618 -7.0
Kentucky Parks -0.4360691 28.5
California Transit -0.4358952 -42.0
California Residential 0.4348016 14.0
Idaho Transit -0.4346598 -30.0
Pennsylvania Workplace -0.4324669 -36.0
Vermont Residential 0.4308452 11.5
New Jersey Transit -0.4257505 -50.5
South Carolina Workplace 0.4159493 -30.0
Montana Retail_Recreation -0.4145442 -51.0
New Hampshire Residential -0.4111997 14.0
Kansas Parks 0.4050584 72.0
North Dakota Parks 0.4030592 -34.0
Idaho Grocery_Pharmacy -0.3983169 -4.5
Alabama Workplace -0.3960854 -29.0
Montana Transit -0.3892824 -41.0
Maryland Grocery_Pharmacy -0.3853180 -10.0
Maryland Workplace -0.3811664 -35.0
New York Transit -0.3750954 -48.0
Florida Residential 0.3736983 14.0
Arizona Residential 0.3705505 13.0
Alabama Transit -0.3647246 -36.5
Nevada Transit -0.3597757 -20.0
Alabama Grocery_Pharmacy -0.3582702 -2.0
New Mexico Retail_Recreation -0.3579773 -42.5
Pennsylvania Retail_Recreation -0.3542614 -45.0
Wyoming Grocery_Pharmacy -0.3539354 -9.0
California Parks -0.3525855 -38.5
Arizona Transit 0.3396695 -38.0
Montana Grocery_Pharmacy -0.3279939 -16.0
Pennsylvania Parks 0.3268933 13.0
Idaho Retail_Recreation -0.3267680 -40.5
Nebraska Grocery_Pharmacy 0.3231969 -0.5
Michigan Parks 0.3226282 30.0
Vermont Retail_Recreation 0.3201130 -57.0
Minnesota Transit -0.3168387 -28.5
Alaska Retail_Recreation 0.3115519 -39.0
Utah Retail_Recreation -0.3041384 -40.0
North Carolina Grocery_Pharmacy 0.3039921 0.0
California Retail_Recreation -0.2966046 -44.0
North Dakota Workplace 0.2931925 -40.0
Colorado Residential 0.2922894 14.0
Florida Parks -0.2917776 -43.0
Arkansas Retail_Recreation -0.2890768 -30.0
Kansas Workplace 0.2880593 -33.0
Maine Retail_Recreation -0.2876158 -42.0
West Virginia Grocery_Pharmacy -0.2836103 -6.0
Rhode Island Transit -0.2835350 -56.0
California Grocery_Pharmacy -0.2830505 -12.0
Texas Workplace 0.2808468 -32.0
Texas Residential -0.2797431 15.0
Maryland Retail_Recreation -0.2725715 -39.0
California Workplace -0.2703705 -36.0
Nevada Retail_Recreation -0.2669754 -43.0
Mississippi Residential 0.2656550 13.0
Oregon Grocery_Pharmacy 0.2649786 -7.0
Tennessee Residential 0.2647253 11.5
Rhode Island Grocery_Pharmacy 0.2617864 -7.5
Tennessee Workplace -0.2540964 -31.0
Illinois Workplace -0.2539861 -31.0
Maryland Residential 0.2538538 15.0
Texas Parks 0.2529249 -42.0
Georgia Grocery_Pharmacy -0.2509031 -10.0
Wisconsin Parks 0.2505591 51.5
North Carolina Workplace 0.2505356 -31.0
Virginia Transit -0.2501517 -33.0
Nevada Residential 0.2353357 17.0
Pennsylvania Grocery_Pharmacy -0.2345032 -6.0
New York Grocery_Pharmacy -0.2321268 8.0
Arkansas Residential 0.2302329 12.0
North Carolina Transit 0.2299017 -32.0
Michigan Workplace -0.2273023 -40.0
Illinois Parks 0.2218349 26.5
South Carolina Parks -0.2191762 -23.0
Washington Workplace -0.2173764 -38.0
North Carolina Residential 0.2170639 13.0
Oregon Residential 0.2166485 10.5
New Jersey Residential 0.2149263 18.0
North Dakota Grocery_Pharmacy -0.2135189 -8.0
Iowa Transit 0.2051643 -24.0
Alabama Parks 0.2015984 -1.0
Kansas Grocery_Pharmacy -0.1990289 -14.0
Missouri Residential -0.1976318 13.0
South Dakota Transit -0.1945599 -40.0
Missouri Workplace 0.1911591 -28.5
Mississippi Grocery_Pharmacy -0.1911394 -8.0
Georgia Workplace -0.1910485 -33.5
Colorado Parks -0.1817729 2.0
Vermont Workplace -0.1806653 -43.0
Illinois Residential 0.1785282 14.0
Oklahoma Parks -0.1730733 -18.5
Texas Transit 0.1726762 -41.5
Virginia Grocery_Pharmacy -0.1703479 -8.0
New Mexico Transit 0.1683497 -38.5
Virginia Parks 0.1675989 6.0
Oklahoma Residential 0.1673044 15.0
Georgia Retail_Recreation -0.1670907 -41.0
Georgia Residential -0.1660811 13.0
Ohio Transit 0.1658647 -28.0
North Dakota Transit 0.1629222 -48.0
Massachusetts Transit -0.1594584 -45.0
West Virginia Workplace 0.1580103 -33.0
Wisconsin Residential -0.1577304 14.0
Virginia Residential 0.1573190 14.0
Washington Transit -0.1562108 -33.5
South Carolina Residential -0.1552988 12.0
Wyoming Retail_Recreation -0.1536400 -40.0
Florida Workplace -0.1502747 -33.0
Michigan Retail_Recreation -0.1501780 -53.0
Maine Residential -0.1499687 11.0
South Dakota Retail_Recreation -0.1455905 -38.5
West Virginia Residential -0.1427500 11.0
Indiana Residential 0.1419037 12.0
Washington Residential 0.1418596 13.0
Maine Grocery_Pharmacy -0.1407362 -13.0
Idaho Residential -0.1389515 11.0
New Hampshire Retail_Recreation -0.1379344 -41.0
Arkansas Transit 0.1354535 -27.0
Ohio Parks -0.1353968 67.5
Massachusetts Residential 0.1353893 15.0
Florida Retail_Recreation 0.1349382 -43.0
Indiana Retail_Recreation 0.1325600 -38.0
Nebraska Parks 0.1315685 55.5
Arkansas Workplace -0.1293799 -26.0
Mississippi Transit -0.1285344 -38.5
Massachusetts Parks 0.1281556 39.0
Pennsylvania Transit -0.1279576 -41.5
Connecticut Parks 0.1271772 43.0
North Carolina Parks -0.1255334 7.0
Alabama Retail_Recreation 0.1244225 -39.0
Idaho Parks 0.1233609 -22.0
Minnesota Workplace -0.1222266 -33.0
Kansas Transit -0.1217196 -26.5
Oregon Parks 0.1207267 16.5
Oregon Retail_Recreation 0.1192767 -41.0
New Hampshire Grocery_Pharmacy -0.1144755 -6.0
Kentucky Transit 0.1139648 -31.0
Mississippi Workplace -0.1135255 -33.0
Washington Grocery_Pharmacy 0.1133734 -7.0
Maryland Transit -0.1120321 -39.0
Mississippi Retail_Recreation -0.1118590 -40.0
Arizona Workplace -0.1085542 -35.0
South Dakota Residential 0.1082829 15.0
Minnesota Parks 0.1082681 -9.0
New Hampshire Transit -0.1079668 -57.0
Ohio Residential 0.1072410 14.0
Michigan Grocery_Pharmacy -0.1072362 -11.0
Indiana Parks -0.1064315 29.0
Wisconsin Grocery_Pharmacy 0.1017321 -1.0
Kentucky Grocery_Pharmacy 0.1014998 4.0
Nebraska Retail_Recreation 0.1009274 -36.0
Pennsylvania Residential 0.0981707 15.0
Oregon Workplace -0.0977432 -31.0
Georgia Parks 0.0949667 -6.0
Washington Parks 0.0931264 -3.5
Wisconsin Workplace -0.0928881 -31.0
New York Residential 0.0916304 17.5
Missouri Transit -0.0892283 -24.5
Minnesota Retail_Recreation 0.0891069 -40.0
South Dakota Grocery_Pharmacy 0.0882891 -9.0
Wyoming Residential 0.0870692 12.5
Oklahoma Grocery_Pharmacy -0.0855342 -1.0
Texas Grocery_Pharmacy 0.0845395 -14.0
Indiana Workplace 0.0836266 -34.0
Virginia Workplace -0.0830367 -31.5
South Carolina Transit 0.0819625 -45.0
Indiana Grocery_Pharmacy -0.0777870 -5.5
Michigan Residential 0.0750125 15.0
Virginia Retail_Recreation -0.0744194 -35.0
Iowa Retail_Recreation -0.0701333 -38.0
Kentucky Retail_Recreation 0.0700902 -29.0
Ohio Retail_Recreation 0.0694048 -36.0
South Carolina Retail_Recreation -0.0688276 -35.0
Ohio Grocery_Pharmacy 0.0681028 0.0
Colorado Transit 0.0667675 -36.0
Nevada Parks 0.0626978 -12.5
Tennessee Transit -0.0617153 -32.0
Iowa Workplace -0.0614430 -30.0
Michigan Transit 0.0588707 -46.0
West Virginia Retail_Recreation 0.0585811 -38.5
Colorado Retail_Recreation -0.0572657 -44.0
Florida Transit -0.0572622 -49.0
Colorado Grocery_Pharmacy -0.0561256 -17.0
Minnesota Grocery_Pharmacy 0.0555282 -6.5
Nevada Workplace 0.0549635 -40.0
Tennessee Parks -0.0540165 10.5
Kentucky Residential 0.0496926 12.0
Washington Retail_Recreation -0.0482327 -42.0
Oklahoma Workplace 0.0480852 -31.0
Texas Retail_Recreation 0.0479734 -40.0
New Hampshire Workplace 0.0472901 -37.0
South Carolina Grocery_Pharmacy 0.0471457 1.0
Illinois Retail_Recreation 0.0469262 -40.0
North Carolina Retail_Recreation 0.0454905 -34.0
Missouri Retail_Recreation -0.0435546 -36.0
Missouri Parks 0.0434333 0.0
Kentucky Workplace -0.0427658 -36.0
South Dakota Workplace 0.0401614 -35.0
Missouri Grocery_Pharmacy 0.0394296 2.0
Nebraska Transit -0.0354045 -9.0
Illinois Grocery_Pharmacy -0.0342025 2.0
Oklahoma Retail_Recreation 0.0310406 -31.0
Florida Grocery_Pharmacy 0.0273626 -14.0
Arizona Parks -0.0267035 -44.5
Tennessee Grocery_Pharmacy 0.0266252 6.0
Oregon Transit 0.0259402 -27.5
Kansas Residential -0.0209443 13.0
Colorado Workplace -0.0197351 -39.0
Iowa Grocery_Pharmacy 0.0187631 4.0
Vermont Transit 0.0180531 -63.0
Wisconsin Retail_Recreation 0.0175933 -44.5
Ohio Workplace -0.0175710 -35.0
Arkansas Grocery_Pharmacy -0.0162037 3.0
Kansas Retail_Recreation -0.0148463 -38.0
Indiana Transit 0.0144230 -29.0
Maryland Parks -0.0119496 27.0
West Virginia Transit -0.0107700 -45.0
Nevada Grocery_Pharmacy 0.0077549 -12.5
Tennessee Retail_Recreation -0.0065322 -30.0
Utah Grocery_Pharmacy -0.0062492 -4.0
Mississippi Parks -0.0062394 -25.0
Alabama Residential 0.0058007 11.0
Georgia Transit -0.0045451 -35.0
New Mexico Workplace 0.0044144 -34.0
Iowa Residential -0.0038259 13.0
Oklahoma Transit 0.0036427 -26.0
Minnesota Residential -0.0036382 17.0
North Dakota Residential -0.0019298 17.0
Alaska Parks NA 29.0
District of Columbia Retail_Recreation NA -69.0
District of Columbia Grocery_Pharmacy NA -28.0
District of Columbia Parks NA -65.0
District of Columbia Transit NA -69.0
District of Columbia Workplace NA -48.0
District of Columbia Residential NA 17.0
# sanity check
ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(x=Total_confirmed_cases.per100,fill=variable))+geom_histogram()+
  facet_grid(~Province.State)+
    default_theme+
  theme(legend.position = "bottom")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

write_plot(mobility.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.plot.png"
write_plot(mobility.global.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.global.plot.png"
(plot_data.permobility_summary.plot<-ggplot(plot_data.permobility_summary,aes(x=variable,y=median_change))+
  geom_jitter(size=2,width=.2)+
  #geom_jitter(data=plot_data.permobility_summary %>% arrange(-abs(median_change)) %>% head(n=15),aes(col=Province.State),size=2,width=.2)+
  default_theme+
  ggtitle("Per-Sate Median Change in Mobility")+
  xlab("Mobility Meaure")+
  ylab("Median Change from Baseline"))

write_plot(plot_data.permobility_summary.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/plot_data.permobility_summary.plot.png"

DELIVERABLE MANIFEST

The following link to commited documents pushed to github. These are provided as a convienence, but note this is a manual process. The generation of reports, plots and tables is not coupled to the execution of this markdown. ## Report This report, html & pdf

Plots

github_root<-"https://github.com/sbs87/coronavirus/blob/master/"

plot_handle<-c("Corona_Cases.world.long.plot",
               "Corona_Cases.world.loglong.plot",
               "Corona_Cases.world.mortality.plot",
               "Corona_Cases.world.casecor.plot",
               "Corona_Cases.city.long.plot",
               "Corona_Cases.city.loglong.plot",
               "Corona_Cases.city.mortality.plot",
               "Corona_Cases.city.casecor.plot",
               "Corona_Cases.city.long.normalized.plot",
               "Corona_Cases.US_state.lm.plot",
               "Corona_Cases.US_state.summary.plot")


deliverable_manifest<-data.frame(
  name=c("World total & death cases, longitudinal",
         "World log total & death cases, longitudinal",
         "World mortality",
         "World total & death cases, correlation",
         "City total & death cases, longitudinal",
         "City log total & death cases, longitudinal",
         "City mortality",
         "City total & death cases, correlation",
         "City population normalized total & death cases, longitudinal",
         "State total cases (select) with linear model, longitudinal",
         "State total cases, longitudinal"),
  plot_handle=plot_handle,
  link=paste0(github_root,"results/",plot_handle,".png")
)


(tmp<-data.frame(row_out=apply(deliverable_manifest,MARGIN = 1,FUN = function(x) paste(x[1],x[2],x[3],sep=" | "))))
##                                                                                                                                                                                                        row_out
## 1                                           World total & death cases, longitudinal | Corona_Cases.world.long.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.long.plot.png
## 2                                 World log total & death cases, longitudinal | Corona_Cases.world.loglong.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.loglong.plot.png
## 3                                                         World mortality | Corona_Cases.world.mortality.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.mortality.plot.png
## 4                                      World total & death cases, correlation | Corona_Cases.world.casecor.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.casecor.plot.png
## 5                                              City total & death cases, longitudinal | Corona_Cases.city.long.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.plot.png
## 6                                    City log total & death cases, longitudinal | Corona_Cases.city.loglong.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.loglong.plot.png
## 7                                                            City mortality | Corona_Cases.city.mortality.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.mortality.plot.png
## 8                                         City total & death cases, correlation | Corona_Cases.city.casecor.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.casecor.plot.png
## 9  City population normalized total & death cases, longitudinal | Corona_Cases.city.long.normalized.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.normalized.plot.png
## 10                     State total cases (select) with linear model, longitudinal | Corona_Cases.US_state.lm.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.lm.plot.png
## 11                                      State total cases, longitudinal | Corona_Cases.US_state.summary.plot | https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.summary.plot.png
row_out<-apply(tmp, 2, paste, collapse="\t\n")
name handle link
World total & death cases, longitudinal Corona_Cases.world.long.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.long.plot.png
World log total & death cases, longitudinal Corona_Cases.world.loglong.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.loglong.plot.png
World mortality Corona_Cases.world.mortality.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.mortality.plot.png
World total & death cases, correlation Corona_Cases.world.casecor.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.world.casecor.plot.png
City total & death cases, longitudinal Corona_Cases.city.long.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.plot.png
City log total & death cases, longitudinal Corona_Cases.city.loglong.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.loglong.plot.png
City mortality Corona_Cases.city.mortality.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.mortality.plot.png
City total & death cases, correlation Corona_Cases.city.casecor.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.casecor.plot.png
City population normalized total & death cases, longitudinal Corona_Cases.city.long.normalized.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.city.long.normalized.plot.png
State total cases (select) with linear model, longitudinal Corona_Cases.US_state.lm.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.lm.plot.png
State total cases, longitudinal Corona_Cases.US_state.summary.plot https://github.com/sbs87/coronavirus/blob/master/results/Corona_Cases.US_state.summary.plot.png

Tables

CONCLUSION

Overall, the trends of COVID-19 cases is no longer in log-linear phase for world or U.S. (but some regions like MD are still in the log-linear phase). Mortality rate (deaths/confirmed RNA-based cases) is >1%, with a range depending on region. Mobility is not a strong indicator of caseload (U.S. data).

See table below for detailed breakdown.

Question Answer
What is the effect on social distancing, descreased mobility on case load?
There is not a strong apparent effect on decreased mobility (work, grocery, retail) or increased mobility (at residence, parks) on number of confirmed cases, either as a country (U.S.) or state level. California appears to have one of the best correlations, but this is a mixed bag
What is the trend in cases, mortality across geopgraphical regions?
The confirmed total casees and mortality is overall log-linear for most countries, with a trailing off beginning for most (inlcuding U.S.). On the state level, NY, NJ, PA starting to trail off; MD is still in log-linear phase. Mortality and case load are highly correlated for NY, NJ, PA, MD. The mortality rate flucutates for a given region, but is about 3% overall.

END

End: ##—— Thu May 21 09:22:51 2020 ——##

Cheatsheet: http://rmarkdown.rstudio.com>

Sandbox

# Geographical heatmap!
install.packages("maps")
library(maps)
library
mi_counties <- map_data("county", "pennsylvania") %>% 
  select(lon = long, lat, group, id = subregion)
head(mi_counties)

ggplot(mi_counties, aes(lon, lat)) + 
  geom_point(size = .25, show.legend = FALSE) +
  coord_quickmap()
mi_counties$cases<-1:2226
name_overlaps(metadata,Corona_Cases.US_state)

tmp<-merge(Corona_Cases.US_state,metadata)
ggplot(filter(tmp,Province.State=="Pennsylvania"), aes(Long, Lat, group = as.factor(City))) +
  geom_polygon(aes(fill = Total_confirmed_cases), colour = "grey50") + 
  coord_quickmap()


ggplot(Corona_Cases.US_state, aes(Long, Lat))+
  geom_polygon(aes(fill = Total_confirmed_cases ), color = "white")+
  scale_fill_viridis_c(option = "C")
dev.off()


require(maps)
require(viridis)

world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white")

head(world_map)
head(Corona_Cases.US_state)
unique(select(world_map,c("region","group"))) %>% filter()

some.eu.countries <- c(
  "US"
)
# Retrievethe map data
some.eu.maps <- map_data("world", region = some.eu.countries)

# Compute the centroid as the mean longitude and lattitude
# Used as label coordinate for country's names
region.lab.data <- some.eu.maps %>%
  group_by(region) %>%
  summarise(long = mean(long), lat = mean(lat))

unique(filter(some.eu.maps,subregion %in% Corona_Cases.US_state$Province.State) %>% select(subregion))
unique(Corona_Cases.US_state$Total_confirmed_cases.log)
ggplot(filter(Corona_Cases.US_state,Date=="2020-04-17") aes(x = Long, y = Lat)) +
  geom_polygon(aes( fill = Total_confirmed_cases.log))+
  #geom_text(aes(label = region), data = region.lab.data,  size = 3, hjust = 0.5)+
  #scale_fill_viridis_d()+
  #theme_void()+
  theme(legend.position = "none")
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
class(world)
ggplot(data = world) +
    geom_sf()

counties <- st_as_sf(map("county", plot = FALSE, fill = TRUE))
counties <- subset(counties, grepl("florida", counties$ID))
counties$area <- as.numeric(st_area(counties))
#install.packages("lwgeom")
class(counties)
head(counties)
ggplot(data = world) +
    geom_sf(data=Corona_Cases.US_state) +
    #geom_sf(data = counties, aes(fill = area)) +
  geom_sf(data = counties, aes(fill = area)) +
   # scale_fill_viridis_c(trans = "sqrt", alpha = .4) +
    coord_sf(xlim = c(-88, -78), ylim = c(24.5, 33), expand = FALSE)


head(counties)
tmp<-unique(select(filter(Corona_Cases.US_state,Date=="2020-04-17"),c(Lat,Long,Total_confirmed_cases.per100)))
st_as_sf(map("county", plot = FALSE, fill = TRUE))

join::inner_join.sf(Corona_Cases.US_state, counties)

library(sf)
library(sp)

nc <- st_read(system.file("shape/nc.shp", package="sf"))
class(nc)


spdf <- SpatialPointsDataFrame(coords = select(Corona_Cases.US_state,c("Lat","Long")), data = Corona_Cases.US_state,
                               proj4string = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))

head(spdf)
class(spdf)
st_cast(spdf)

filter(Corona_Cases.US_state.summary,Date=="2020-04-20" & Province.State %in% top_states_modified)
id

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